Posted
by
Soulskillon Saturday January 30, 2010 @01:15PM
from the this-can-only-end-well dept.

quaith writes "Dario Floreano and Laurent Keller report in PLoS ONE how their robots were able to rapidly evolve complex behaviors such as collision-free movement, homing, predator versus prey strategies, cooperation, and even altruism. A hundred generations of selection controlled by a simple neural network were sufficient to allow robots to evolve these behaviors. Their robots initially exhibited completely uncoordinated behavior, but as they evolved, the robots were able to orientate, escape predators, and even cooperate. The authors point out that this confirms a proposal by Alan Turing who suggested in the 1950s that building machines capable of adaptation and learning would be too difficult for a human designer and could instead be done using an evolutionary process. The robots aren't yet ready to compete in Robot Wars, but they're still pretty impressive."

actually, as of now, these robots are just programs in a physics computer experiment... so if they were to evolve to be smart, we'd have a computer virus instead of an actual robot that is evolving.
I wonder, if a robot program like this were let loose on the internet, and was capable of learning... what would it learn?

actually, as of now, these robots are just programs in a physics computer experiment... so if they were to evolve to be smart, we'd have a computer virus instead of an actual robot that is evolving. I wonder, if a robot program like this were let loose on the internet, and was capable of learning... what would it learn?

I wonder, if a robot program like this were let loose on the internet, and was capable of learning... what would it learn?

It would learn, amongst other things:

A whole lot about sexual possibilities, as well as plenty of impossibilities.
Far too much about Megan Fox, Britney Spears, and Lindsay Lohan.
That editing wikipedia is pointless, no matter how programmed for repetitive tasks you are.
That almost every review of a new product is shilled all over the net.

There's a very good chance that that robot would go out of its way to annihilate us all after what it learned on the Internet.

Lots. I personally prefer robots that aren't "strong AI".Not so much because they may rule over us but more because we aren't already doing a great job with animals, so it'll be irresponsible to create a new class of creatures that we will likely enslave (in contrast I think enslaving "dumb machines" is fine- my car is less likely to feel anything than an ant, or even an amoeba).

Having the focus more on augmenting humans than emulating humans seems a better approach to me.

"The robots evolved" - WTF?! People kept training the neural network to create desirable functionality, without people training the neural network and doing "mutations" those robots would have evolved as much as a lightbulb stuck up somebody's arse!

Isn't that what evolution is all about?
Changing to get desirable functionality/traits, that is, not shoving lightbulb up people's arses.

Just guessing but perhaps what the GP is complaining about is the idea of "people" training the robots, i.e. an outside and consciously directed force is involved. Whereas evolution is a random process with no guiding intelligence behind it. Or maybe he meant something else.

So, a group made some beings in a 'universe' that are unable to see their creators. The beings were made self replicating, and were fiddled with. Then group withdrew and left the beings to their devices. Eventually beings denied being created in first place.

If you like the article, try this one: Teamwork in Genetic Programming [lalena.com].
I did this 15 years ago, but unfortunately I didn't have access to real robots. Just computer simulation.
Simulated ants used teamwork to lift heavy pieces of food - if they all stopped and waited at the first food they found they would wait forever because there weren't enough of them. Had to have some intelligence.
There were also some water crossing problems where some ants (but not all) had to sacrifice themselves to build a bridge

Compared to the rest of the summary, which says: "The authors point out that this confirms a proposal by Alan Turing who suggested in the 1950s that building machines capable of adaptation and learning would be too difficult for a human designer and could instead be done using an evolutionary process. The robots aren't yet ready to compete in Robot Wars, but they're still pretty impressive." getting the journal wrong is a pretty trivial error.

These machines were designed and built by humans to be capable of adaptation and learning, so it actually proves Turing's thesis false. They then use the adaptation and learning capability their human designers built into them to adapt and learn, but according to the very next sentence don't produce outcomes that are as good as purely human-designed ones.

So why bring Turing's name into it at all? I suspect marketing has something to do with it. Which is too bad, because the results themselves are quite interesting, although I'm curious how the robots reproduce... if this actually an evolutionary system rather than a merely adaptive/learning one. For the confused: growing children do not "evolve", except in the loosest and least interesting metaphorical sense. They learn. As near as I can tell these robots do the same thing.

These machines were designed and built by humans to be capable of adaptation and learning

Not really. The experimenter himself reprogrammed the robots at each generation, using selection criteria that he specified. Essentially, he implemented trial-and-error selection of input weights using random reweighting between trials. This is more about design strategies than about biology, and it says that even a monkey randomly adjusting the gains of a control system will eventually develop a control system that works.

worth RTF'ing for a better idea of how this is done (btw, they are the same robots that were taught to "deceive" other robots about where the "food" is).
Plus, the video of the Predator/Prey stalemate is just epic!
As for the 3rd video (maze navigation), man, i would have blown these 1st gen robots to pieces before they could say Darwin!

Definitely an interesting continuation of work being done by various groups over the past couple of decades.

But one thing to note is that crossover isn't especially useful in neural network evolution. In early stages of evolution, it's really no better than random large perturbation of large swaths of the genome. In later stages, it can actually decrease the speed of evolution toward high fitness genomes, because at least some of the time (particularly if there are multiple "species" in the population) crossover ends up being a random large perturbation which hinders the search of local fitness space by mutation; the rest of the time (when individuals from the same "species" are crossed) crossover is no better than mutation.

The reason for this is because the parameters of a neural network are not functional. A section of the genome may correspond to a weight between neurons, but that weight doesn't have a specific function. In biological organisms, each gene is transcribed/translated into a protein, and that protein may have a particular function within the cell. If that gene is acquired by a descendant through crossover, the protein could serve the same (or a somewhat modified) role it served in its parent, even if the rest of the descendant's genome was acquired from the other parent. But with artificial neural networks, the parameters were all evolved as parts of a whole, where each individual parameter has no function on its own, but the behavior emerges from having all of those parameters at the same time.

This could potentially be mitigated by the genome encoding scheme one uses, and of course, if the crossover rate is low enough, the ultimate effect would be small.

Nothing special about neural networks... I achieved similar results [mangocats.com] with a made up scheme of decision weight equations that were "genetically developed" in a big breeding tank.

Basically, behavior that allows greater procreation tends to appear spontaneously, and behavior that cuts procreation short tends to disappear. My "bugs" exhibited a clear shift in behavior to collision avoidance because collisions resulted in death for one of them. I was watching for "sniper bugs" that got good at colliding with

Did any of your bugs evolve to the point where they learned during the course of their lifespan, as opposed to genetic learning/memory? What I mean is, did your bugs know not to collide (for instance) because they saw another bug get killed by colliding, or were the genetic markers that predisposed bugs to collide with things removed from the genome over time?
I ask this because while robots evolving is neat, I don't see what path it would follow to producing any real "AI" in the sense of something recogni

No, their behavior was programmed at birth, senses were limited to about 60 "floating point channels" which sounds like a lot, but in reality isn't much to observe the world with. They could potentially learn neural net style from painful experiences, but I didn't go that way- a successful program would replicate better than an unsuccessful one, kind of a circular success criteria, but, that's life too.

Your point is valid if the genotype to phenotype mapping is a simple mapping to neuron type, connection type, weights, etc. However, we clearly have effective crossover in humans, which means there can be a genotype to phenotype mapping that operates at more of a functional level. It's an interesting and difficult problem.

Are they actually genetically evolving a traditional neural network though? The article made it sound that way but it was light on details. I know the words they were using but I don't know if the author knew what they meant.

There is a possibility they are just using traditional genetic algorithm stuff where the "neurons" actually represent programming logic and not just simple weight values like what a "neural network" typically is.

What's wrong with deriving a new noun from the verb? It may be redundant at this point for this word (or they may have a more specific meaning in mind than the general "orient"), but it's hardly unprecedented.

Because it's confusing as hell when people think they invented a new word, but all they did was assign new meaning to an already existing word. If you slept through English class, it might be useful, but to others who know current rules of grammar, spelling and vocabulary, it's just confusing and a sign of ignorance.

I wasn't confused. You clearly weren't confused because you quickly were able to figure out the original. The meaning is quite clear. At worst, one might ask why not use "orient", but otherwise, the meaning is perfectly clear, or perhaps more precise.

Of course, "orient" as a verb is actually no better, because it was originally noun and was then "verbed". I'm sure if you had been around in those days, you would have pulled the same annoying pedantry out of your ass.

Good god, man. Don't you realize what you're proposing? Next thing you know someone will coin a new verb, "orientatation" from your new noun. And from there we're just another smart-ass/.'er away from getting another new noun, "orientatatate". I think you see where I'm going with this. Smug linguists would take over, innovating a cascade of new words that would fill the English language, only to eventually collapse into a recursive singularity of hypothetical new words. Spell-checkers would all overf

The noun "orientation [reference.com]" is derived from the verb "orient [reference.com]", not the other way around.

Thanks god someone mentioned that! I absolutely hate when people use that "word". It's just... wrong. It's like when people say "funner". Who cares if you know what the person meant, they're still butchering English and if they're a native speaker, that's just ridiculous.-Taylor

You're correct. I wasn't trying to invent a new word. Should have used "orient". Just sloppy editing on my part -- I started with a sentence that had "orientation" in it and shortened it to "orientate" while I was reworking it. Sloppy, very sloppy.

Just blew off a wannabe guide in Bocas city in favor of one who was a little more relaxed. So far I've bought him a beer, there's been no hard sell. The places he took us (bar, hotel, bar with strong drinks) have all undercharged us compared to the menu.

Obviously, you can develop a resistance to the hard sell, and it can pay off.

This kind of behavior was first demonstrated/modeled (AFAIK/IIRC) as part of the Tierra [ou.edu] simulations almost twenty years ago. Though I don't have a reference to hand, I know it's been done in neural networks before too.

So other than the 'sizzle' (as opposed to 'steak') of doing it with robots, can anyone explain what is new here?

Spoiler for the story - since it's basically the ending - but the point in question:

As the Tasso models approach, Hendricks notices the bombs clipped to their belts, and recalls that first Tasso used one to destroy other claws. At his end, Hendricks is vaguely comforted by the thought that the claws are designing, developing, and producing weapons meant for killing other claws.

I thought you might have male and female algorithms for some optimization problems "walking around" in a virtual world, mate to create combined algorithms, giving you some kind of blood-relationship between the algorithms (and yes, they should die after some time). the related algorithms would form "clans" and if males of opposing clans meet, they fight over each others ressources (RAM and CPU-Cycles) and there should be sources of these ressources, which should dry out over time, so the algorithms HAVE to

A simulation I developed around 1987 had 2D robots that duplicated themselves from a sea of parts. They would build themselves up and then cut themselves apart to make two copies. To my knowledge, it was the first 2D simulation of self-replicating robots from a sea of parts. The first time it worked, one robot started canibalizing the other to build itself up again. I had to add a sense of "smell" to stop robots from taking parts from their offspring. As another poster referenced, Philip K. Dick's point on identity in 1953 was very prescient:http://en.wikipedia.org/wiki/Second_Variety [wikipedia.org]"Dick said of the story: "My grand theme -- who is human and who only appears (masquerading) as human? -- emerges most fully. Unless we can individually and collectively be certain of the answer to this question, we face what is, in my view, the most serious problem possible. Without answering it adequately, we cannot even be certain of our own selves. I cannot even know myself, let alone you. So I keep working on this theme; to me nothing is as important a question. And the answer comes very hard.""

However, those robots were not evolving. I presented a talk on that simulation at a workshop on AI and Simulation in 1988 in Minnesota, saying how hard easy it was to make robots that were destructive, but how much harder it would be to make them cooperative. A major from DARPA literally patted me on the back and told me to "keep up the good work". To his credit, I'm not sure which aspect (destructive or cooperative) he was talking about working on.:-) But I left that field around that time for several reasons (including concerns about military funding and use of this stuff, but also that it seemed like we knew enough to destroy ourselves with this stuff but not enough to make it something wonderful). At the same workshop someone presented something on a simulation of organisms with neural networks that learned different behaviors. A professor I took a course from at SUNY Stony Brook has done some interesting stuff on evolution and communications with simple organisms:http://www.stonybrook.edu/philosophy//faculty/pgrim/pgrim_publications.html [stonybrook.edu]Anyway, in the quarter century almost since then, what I have learned is that the greatest challenge of the 21st century is the tools of abundance like self-replicating robots (or nanotech, biotech, nuclear energy, networking, bureaucracy, and others things) in the hands of those still preoccupied with fighting over percieved scarcity, or worse, creating artificial scarcity. What could be more ironic than using nuclear missiles to fight over Earthly oil fields, when the same sorts of techology and organizations could let us build space habitats and big renewable energy complexes (or nuclear power too). What is more ironic than building killer robots to enforce social norms related to forcing people to sell their labor doing repetitive work in order to gain the right to consume, rather than just build robots to do the work? Anyway, it won't be the robots that kill us off. It will be the unexamined irony.:-)

I've used the same programming mechanism and it works
but its not learning or anything close. They create a
neural network for each robot brain, then wipe the brain
if it doesn't work well enough, and breed from the ones
that work well. The population of robots learn by
evolution, but each individual one, can't learn at all.
Real animal and people of course can learn, and
learn well, in there own lifetimes. So this learn
mechanism is far inferer to natural brains.

Having done such simulations as well, I can tell you it's possible to integrate Hebbian learning in there. There has also been research work combining evolving the neural network's structure and using backpropagation to adjust weights. Finally, in my own experiments, I showed that agents with recurrent neural networks can learn without either of those things. It's essentially possible to build the neural network equivalent of a flip-flop. The agent can then turn these neural switches on and off during its l